Fusing Visual and Inertial Sensors with Semantics for 3D Human Pose Estimation
- Submitting institution
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The University of Surrey
- Unit of assessment
- 33 - Music, Drama, Dance, Performing Arts, Film and Screen Studies
- Output identifier
- 9007966_2
- Type
- D - Journal article
- DOI
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10.1007/s11263-018-1118-y
- Title of journal
- International Journal of Computer Vision
- Article number
- -
- First page
- 381
- Volume
- 127
- Issue
- 4
- ISSN
- 0920-5691
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2018
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- 12 - Engineering
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
-
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Factual information about significance: This paper led the way in MVV+IMU pose estimation and inspired several other researchers to take a combined visual+inertial approach to pose estimation and motion capture. The technique's improvement on previous state-of-the-art accuracy and its groundbreaking viability for outdoor use has led to interest from Intel in using it to capture athletes at the Olympics. Additionally, the TotalCapture dataset has been used extensively, including in the development of systems for horse motion capture, elderly fall detection and healthcare rehabilitation.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
